With the extensive use of structural health monitoring technologies, vibration-based structural damage detection becomes a crucial task in both academic and industrial communities. Following the noteworthy trends of data-driven paradigms in recent years, some solutions have been released to identify, localize, and classify damages via deep neural networks. However, some deficiencies still exist for effective damage-intensive feature extraction and representation. To overcome such a problem, this paper proposes a novel end-to-end structural damage detection neural model by taking the advantages of the Convolutional Neural Network and Bidirectional Gated Recurrent Unit in parallel. The well-known IASC-ASCE benchmark and TCRF dataset are used for evaluation. The experimental results show that the proposed approach can achieve a better detecting effect than other existing manners.

随着结构健康监测技术的广泛使用，基于振动的结构损伤检测已成为学术界和工业界的关键任务。随着近年来数据驱动范例的显着趋势，已经发布了一些解决方案，可通过深度神经网络来识别，定位和分类破坏。但是，对于有效的破坏密集型特征提取和表示，仍然存在一些缺陷。为了克服这个问题，本文利用卷积神经网络的优点，提出了一种新颖的端到端结构损伤检测神经模型。和双向门控递归单元并联。评估使用了著名的IASC-ASCE基准和TCRF数据集。实验结果表明，与其他现有方法相比，该方法具有更好的检测效果。